System and method for detecting retinopathy

ABSTRACT

A system and computer-implemented method for detecting retinopathy is provided. The system comprises an image input module configured to receive one or more fundus images. Further, the system comprises a pre-processing module configured to apply one or more transformations to the one or more received fundus images. Furthermore, the system comprises a feature extraction module configured to extract one or more features from the one or more transformed images using one or more Convolutional Neural Networks (CNNs). Also, the system comprises a prediction module configured to determine stage of retinopathy by classifying the one or more extracted features using pre-stored features, wherein the pre-stored features are extracted from one or more training fundus images by the one or more CNNs and further wherein each pre-stored feature corresponds to a class which is associated with a predetermined stage of retinopathy.

CROSS REFERENCE TO RELATED APPLICATION

This application is related to and claims the benefit of Indian PatentApplication No. 201741003340 filed on Jan. 30, 2017, the contents ofwhich are herein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates generally to detecting retinopathy. Moreparticularly, the present invention provides a system and method forautomatically and accurately detecting retinopathy.

BACKGROUND OF THE INVENTION

Diabetic retinopathy is an eye disease which is associated withlong-standing diabetes. Vision impairment may be prevented using lasertreatments if diabetic retinopathy is detected early. However, earlydetection of diabetic retinopathy is challenging as diabetic retinopathydoes not show explicit symptoms until it reaches advance stages.

Conventionally, diabetic retinopathy is detected manually by physiciansand ophthalmologists. However, manual detection of diabetic retinopathyhas many disadvantages such as lack of experience. Also, manuallydetecting diabetic retinopathy is a time-consuming process. Further,delay in screening process leads to delayed or no follow-up,miscommunication and delayed treatment thereby increasing theprobability of vision loss.

To overcome the disadvantages of manual detection, systems and methodsexist that facilitate automatic detection of retinopathy. For instance,systems exist that use training data to build datasets and algorithmsfor detecting retinopathy from digital fundus images. However, theabove-mentioned systems also suffer from various disadvantages. Theabovementioned systems are incapable of processing noisy images, out offocus images, underexposed and overexposed images. Also, these systemsare not able to predict retinopathy with certainty thereby facilitatingneed of a confirmatory screening by specialists.

In light of the abovementioned disadvantages, there is a need for asystem and method for automated detection of retinopathy, particularlydiabetic retinopathy. Further, there is a need for a system and methodwhich is capable of efficiently processing images captured from colorfundus camera for detecting retinopathy. Furthermore, there is a needfor a system and method capable of accurately detecting retinopathy andif required, promptly referring the patients to specialists. Inaddition, there is a need for a learning based system and method thatuses pattern recognition with feedback loop. Also, there is a need for asystem and method that is scalable, cost-effective, capable ofprocessing multiple images, lowers dependency on human intervention andfacilitates in providing more time to medical practitioners.

SUMMARY OF THE INVENTION

A system, computer-implemented method and computer program product fordetecting retinopathy is provided. The system comprises an image inputmodule configured to receive one or more fundus images. Further, thesystem comprises a pre-processing module configured to apply one or moretransformations to the one or more received fundus images. Furthermore,the system comprises a feature extraction module configured to extractone or more features from the one or more transformed images using oneor more Convolutional Neural Networks (CNNs). Also, the system comprisesa prediction module configured to determine stage of retinopathy byclassifying the one or more extracted features using pre-storedfeatures, wherein the pre-stored features are extracted from one or moretraining fundus images by the one or more CNNs and further wherein eachpre-stored feature corresponds to a class which is associated with apredetermined stage of retinopathy.

In an embodiment of the present invention, the one or moretransformations comprise at least one of: contrast stretching and huetransformation. In an embodiment of the present invention, the one ormore features are extracted from the one or more transformed images bypassing the one or more transformed images through one or more layers ofthe CNNs thereby extracting details corresponding to one or more pointsof interest within the one or more transformed images.

In an embodiment of the present invention, the one or more extractedfeatures are classified by matching each of the one or more extractedfeatures with the pre-stored features and further wherein the stage ofretinopathy is determined based on the predetermined stage ofretinopathy associated with the class corresponding to each of thematched pre-stored features.

In an embodiment of the present invention, the one or more fundus imagesare of one or more patients. In an embodiment of the present invention,each training fundus image represents a specific predetermined stage ofretinopathy and is classified based on the specific predetermined stageof retinopathy and further wherein the pre-stored features extractedfrom the training fundus image corresponds to the class associated withthe training fundus image. In an embodiment of the present invention,the predetermined stage of retinopathy is one of: absence ofretinopathy, mild retinopathy, moderate retinopathy, severe retinopathyand proliferative retinopathy.

The computer-implemented method for detecting retinopathy, via programinstructions stored in a memory and executed by a processor, comprisesreceiving one or more fundus images. The computer-implemented methodfurther comprises applying one or more transformations to the one ormore received fundus images. Furthermore, the computer-implementedmethod comprises extracting one or more features from the one or moretransformed images using one or more Convolutional Neural Networks(CNNs). In addition, the computer-implemented method comprisesdetermining stage of retinopathy by classifying the one or moreextracted features using pre-stored features, wherein the pre-storedfeatures are extracted from one or more training fundus images by theone or more CNNs and further wherein each pre-stored feature correspondsto a class which is associated with a predetermined stage ofretinopathy.

The computer program product for detecting retinopathy comprises anon-transitory computer-readable medium having computer-readable programcode stored thereon, the computer-readable program code comprisinginstructions that when executed by a processor, cause the processor toreceive one or more fundus images. The processor further applies one ormore transformations to the one or more received fundus images.Furthermore, the processor extracts one or more features from the one ormore transformed images using one or more Convolutional Neural Networks(CNNs). The processor also determines stage of retinopathy byclassifying the one or more extracted features using pre-storedfeatures, wherein the pre-stored features are extracted from one or moretraining fundus images by the one or more CNNs and further wherein eachpre-stored feature corresponds to a class which is associated with apredetermined stage of retinopathy.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The present invention is described by way of embodiments illustrated inthe accompanying drawings wherein:

FIG. 1 is a block diagram illustrating a system for automaticallydetecting retinopathy, in accordance with an embodiment of the presentinvention;

FIG. 2 is a detailed block diagram illustrating a feature extractionmodule, in accordance with an exemplary embodiment of the presentinvention;

FIG. 3 is a flowchart illustrating a system for automatically detectingretinopathy, in accordance with an embodiment of the present invention;and

FIG. 4 illustrates an exemplary computer system for automaticallydetecting retinopathy, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

A system and method for automated detection of retinopathy is describedherein. The invention provides a system and method which is capable ofefficiently processing images of eye captured using color fundus camerafor detecting retinopathy. The invention further provides a system andmethod capable of accurately detecting retinopathy and if required,promptly referring the patients to specialists. In addition, theinvention provides a learning based system and method that uses patternrecognition with feedback loop. Also, the invention provides a systemand method that is scalable, cost-effective, capable of processingmultiple images, lowers dependency on human intervention and facilitatesin providing more time to medical practitioners.

The following disclosure is provided in order to enable a person havingordinary skill in the art to practice the invention. Exemplaryembodiments are provided only for illustrative purposes and variousmodifications will be readily apparent to persons skilled in the art.The general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the invention. Also, the terminology and phraseology used is for thepurpose of describing exemplary embodiments and should not be consideredlimiting. Thus, the present invention is to be accorded the widest scopeencompassing numerous alternatives, modifications and equivalentsconsistent with the principles and features disclosed. For purpose ofclarity, details relating to technical material that is known in thetechnical fields related to the invention have not been described indetail so as not to unnecessarily obscure the present invention.

The present invention would now be discussed in context of embodimentsas illustrated in the accompanying drawings.

FIG. 1 is a block diagram illustrating a system 100 for automaticallydetecting retinopathy, in accordance with an embodiment of the presentinvention. The system 100 comprises an image input module 102, apre-processing module 104, a feature extraction module 106, a predictionmodule 108 and an output module 110.

The system 100 is initially trained using one or more training fundusimages. During training, the one or more training fundus images arereceived and classified by the image input module 102. In an embodimentof the present invention, the one or more training fundus images arehigh resolution images of the fundus taken using different cameras undervarious imaging conditions. Further, each of the one or more receivedtraining fundus images represent a specific predetermined stage ofretinopathy and are classified based on the specific predetermined stageof retinopathy. In an exemplary embodiment of the present invention, thetraining fundus images are classified using numbers 0-4, wherein imagesof the eye fundus that have no retinopathy (absence of retinopathy)belong to class 0, images of the eye fundus that have mild retinopathybelong to class 1, images of the eye fundus that have moderateretinopathy belong to class 2, images of the eye fundus that have severeretinopathy belong to class 3 and images of the eye fundus that haveproliferative retinopathy and belong to class 4.

After classification, the one or more classified images are forwarded tothe pre-processing module 104 by the image input module 102. Thepre-processing module 104 is configured to apply one or moretransformations to the one or more classified images. In an embodimentof the present invention, the one or more transformations include, butnot limited to, contrast stretching and hue transformation. Contraststretching is an image enhancement technique which improves the contrastof an image by stretching the range of intensity values to span adesired range of values. Another transformation is hue. The RGB colorspace has no intrinsic relation to the natural color properties andneither to human interpretation of color, however hue represents howhumans perceive color. Hue is one of the main properties (called colorappearance parameters) of a color. Applying the hue transformation makesthe algorithm less sensitive if not invariant to lighting variations.

After applying the one or more transformations, the pre-processingmodule 104 creates two versions of each of the one or more classifiedimages. All the images are then normalized to represent each classequally in the training process.

The feature extraction module 106 comprises of one or more ConvolutionalNeural Networks (CNNs) configured to extract features from the one ormore normalized images received from the pre-processing module 104. Thearchitecture of a CNN is designed to take advantage of the structure ofan input image. This is achieved with local receptive fields and sharedweights followed by pooling which results in translation invariantfeatures. Another advantage of using CNNs is self-feature extraction.CNN based Feature exaction facilitates in capturing low, mid and highlevel image features automatically.

During feature extraction, details from one or more points of interestwithin the one or more normalized images are extracted intelligently andstored as features. Further, each stored feature from the one or morenormalized images corresponds to the class associated with thenormalized image from which it was extracted.

In an exemplary embodiment of the present invention, the featureextraction module 106 comprises of five CNNs. Further, by using fiveCNNs, the feature extraction module 106 facilitates each CNN to extractfeatures specific to a class representing a specific stage ofretinopathy and avoiding loss of information. Furthermore, each CNN isdesigned to concentrate on a particular stage of the disease and learnmorphological differences between images of each stage of retinopathythereby improving performance by using features extracted from five CNNsinstead of only one CNN.

Each CNN is inputted with a set of images belonging to a particularclass and representing a specific stage of retinopathy. For instance,the first CNN is inputted with normalized images of the fundus having noretinopathy that belong to class 0, the second CNN is inputted withnormalized images of the fundus having mild retinopathy that belong toclass 1 and so on. Further, classifying the one or more training fundusimages facilitates in extracting features and class probabilities of theextracted features by corresponding CNN thereby facilitating efficientidentification of specific stage of the disease. The feature extractionmodule is discussed in detail in conjunction with FIG. 2.

FIG. 2 is a detailed block diagram illustrating a feature extractionmodule 200, in accordance with an embodiment of the present invention.

In an embodiment of the present invention, the feature extraction module200 comprises of five CNNs. Further, each CNN comprises a firstconvolutional layer 202, a first pooling layer 204, a secondconvolutional layer 206, a second pooling layer 208, a first fullyconnected layer 210 and a second fully connected layer 212.

The first convolutional layer 202 consist of a rectangular grid ofneurons configured to apply weights in the form of potential filters onthe one or more normalized images received from the pre-processingmodule 104 (FIG. 1). After the one or more normalized images passthrough the first convolutional layer 202, the first pooling layer 204subsamples output of the first convolutional layer 202 to produce asingle output. In an embodiment of the present invention, the firstpooling layer 204 uses maximum pooling to take maximum block forpooling. The output of the first pooling layer 204 is then provided asinput to the second convolutional layer 206. The second convolutionallayer 206 also consists of a rectangular grid of neurons which appliesweights in the form of potential filters to the input received from thefirst pooling layer 204 and forwards the output to the second poolinglayer 208. The second pooling layer 208, similar to the first poolinglayer 204, subsamples the output of the second convolutional layer 206.After pooling, the control is transferred to the first fully connectedlayer 210.

The first fully connected layer 210 connects each of its neurons witheach neuron of the second pooling layer 208. Further, the first fullyconnected layer 210 provides a one-dimensional representation offeatures extracted from the one or more normalized images. The output ofthe first fully connected layer 210 acts as an input to the second fullyconnected layer 212. Further, the second fully connected layer 212 alsoprovides one-dimensional representation of features extracted from theone or more normalized images. Furthermore, using two fully connectedlayers facilitate in reducing under-fitting of the neural network,providing non-linear functionality, increasing feature learnability,increasing generalization, increasing accuracy and feature hierarchy tolearn distinct set of features in each layer. In an embodiment of thepresent invention, the CNNs further comprise a softmax function modulewhich is used for multiclass classification. The softmax functionfacilitates in categorical distribution that is a probabilitydistribution over various different possible outcomes.

In an exemplary embodiment of the present invention, the first CNN is amulticlass classification model with classes as 0 vs 1 vs 2 vs 3 vs 4.The first CNN extracts 150 features and 5 class probabilities from eachimage. Further, since two versions of each image are processed therebytotal of 310 features are extracted. The second is a binaryclassification model with classes 0 vs 1 vs 2, 3 and 4. The second CNNextracts 150 features and 3 class probabilities from each image therebya total of 306 features for two versions of each image. The third CNN isalso a binary classification model with classes 0, 1 vs 2, 3, and 4. Thethird CNN extracts 150 features and 2 class probabilities from eachimage thereby a total of 304 features for two versions of each image.The fourth CNN is also a binary classification model with classes 0, 1,2 vs 3, 4. The fourth CNN extracts 150 features and 2 classprobabilities from each image thereby a total of 304 features for twoversions of each image. The fifth CNN is also a binary classificationmodel with classes 0 vs 1, 2 vs 3, 4. The fifth CNN extracts 150features and 3 class probabilities from each image thereby a total of306 features for two versions of each image. The features and the classprobabilities extracted by the five CNNs are stored in the predictionmodule (FIG. 1) and are used during detection process.

In an embodiment of the present invention, the prediction module 108 isalso trained using the features and class probabilities extracted duringthe training process. After training, the prediction module 108 isconsidered trained for detecting the stage of the disease in the form ofclass probabilities.

Referring back to FIG. 1, the system 100 is considered to be trainedafter extraction of features from the one or more training fundusimages. Further, the trained system 100 is capable of detectingretinopathy using one or more fundus images of one or more patients.

During the detection process, the image input module 102 is configuredto receive and forward the one or more fundus images of the one or morepatients to the pre-processing module 104. The pre-processing module 104is configured to apply one or more transformations to the one or morereceived fundus images. After applying the transformations, the one ormore transformed images are forwarded to the CNNs within the featureextraction module 106 for feature extraction. Further, one or morefeatures are extracted from the one or more transformed images bypassing the one or more transformed images through one or more layers ofthe CNNs thereby extracting details corresponding to one or more pointsof interest (referred to as features) within the one or more transformedimages. The features extracted by the CNNs are then forwarded to theprediction module 108.

The prediction module 108 comprises a classifier that uses pre-storedfeatures and their corresponding class probabilities extracted by theCNNs during training process to predict the stage of the disease. Theprediction module 108 passes the features, extracted during thedetection process, through the classifier to determine stage ofretinopathy by classifying the one or more extracted features usingpre-stored features. Further, the one or more extracted features areclassified by matching each of the one or more extracted features withthe pre-stored features. Once an extracted feature matches with apre-stored feature, the class corresponding to the matched pre-storedfeature is associated with the corresponding extracted feature. Theprediction module 108 then determines stage of retinopathy of the one ormore patients based on the predetermined stage of retinopathy associatedwith the class corresponding to each of the matched pre-stored features.In an embodiment of the present invention, the prediction module 108considers the class corresponding to each of the one or more matchedpre-stored features to determine the stage of retinopathy. In anembodiment of the present invention, the prediction module 108determines the stage of retinopathy for a fundus image of a patient asthe predetermined stage of retinopathy associated with the classcorresponding to maximum matched pre-stored features of the fundusimage.

In an embodiment of the present invention, the prediction module 108contains a classification model which ensures bias-variance trade-off byemploying regularization and boosting. Further, regularizationfacilitates in reduces over-fitting model (variance) and boostingfacilitates in reducing under-fitting (Bias). Further, the predictionmodule 108 applies greater than condition to identify presence orabsence of retinopathy in the one or more fundus images provided for thedetection process.

The output module 110 is configured to provide the results of thedetection process to one or more users of the system 100. In anembodiment of the present invention, the one or more users of the system100 comprise, the one or more patients, one or more ophthalmologists,one or more physicians and any other stakeholder/concerned person. In anembodiment of the present invention, the output module 110 is configuredto provide the results of the detection process in various formats. Inan exemplary embodiment of the present invention, the results areprovided in the form of a report in Portable Document Format (PDF). Inan embodiment of the present invention, the results are provided to theone or more users automatically via one or more communication channelsin real-time. Further, the one or more communication channels include,but not limited to, electronic mail, Short Messaging Service (SMS) andinstant messaging services.

In an embodiment of the present invention, the system 100 is capable ofprocessing multiple images at a time. In an exemplary embodiment of thepresent invention, the system 100 processes twenty images at a time. Inan embodiment of the present invention, the system 100 uses patternrecognition with feedback loop. In an embodiment of the presentinvention, the system 100 is a cloud based server. Further, the system100 comprises an enabled Graphics Processing Unit (GPU) accelerator.Furthermore, image processing libraries are installed and used by thepre-processing module 104 to transform the labeled images beforetraining. Also, the feature extraction module 106 uses a deep learningframework to train feature extraction model with transformed images andsaves the feature extraction model for later use.

FIG. 3 is a flowchart illustrating a system for automatically detectingretinopathy, in accordance with an embodiment of the present invention.

At step 302, one or more fundus images of one or more patients arereceived for determining presence or absence of retinopathy. At step304, one or more transformations are applied to the one or more receivedfundus images. In an embodiment of the present invention, the one ormore transformations comprise at least one of: contrast stretching andhue transformation.

At step 306, one or more features from the one or more transformedimages are extracted using one or more Convolutional Neural Networks(CNNs). In an embodiment of the present invention, the one or morefeatures are extracted from the one or more transformed images bypassing the one or more transformed images through one or more layers ofthe CNNs thereby extracting details corresponding to one or more pointsof interest within the one or more transformed images. In an exemplaryembodiment of the present invention, five CNNs are employed for featureextraction. In an exemplary embodiment of the present invention, eachCNN comprises two convolutional layers, two pooling layers and two fullyconnected layers.

At step 308, stage of retinopathy is determined by classifying the oneor more extracted features using pre-stored features. In an embodimentof the present invention, the pre-stored features are extracted from oneor more training fundus images during a training process by the one ormore CNNs. Further, each pre-stored feature corresponds to a class whichis associated with a predetermined stage of retinopathy. In an exemplaryembodiment, the predetermined stage of retinopathy is one of: absence ofretinopathy, mild retinopathy, moderate retinopathy, severe retinopathyand proliferative retinopathy.

In an embodiment of the present invention, during the training process,each training fundus image represents a specific predetermined stage ofretinopathy and is classified based on the specific predetermined stageof retinopathy. Further, the pre-stored features extracted from thetraining fundus image corresponds to the class associated with thetraining fundus image.

For determining the stage of retinopathy, the one or more extractedfeatures are classified by matching each of the one or more extractedfeatures with the pre-stored features. Further, the stage of retinopathyis determined based on the predetermined stage of retinopathy associatedwith the class corresponding to each of the matched pre-stored features.In an embodiment of the present invention, stage of retinopathy for afundus image of a patient is determined as the predetermined stage ofretinopathy associated with the class corresponding to maximum matchedpre-stored features of the fundus image.

FIG. 4 illustrates an exemplary computer system for automaticallydetecting retinopathy, in accordance with an embodiment of the presentinvention.

The computer system 402 comprises a processor 404 and a memory 406. Theprocessor 404 executes program instructions and may be a real processor.The processor 404 may also be a virtual processor. The computer system402 is not intended to suggest any limitation as to scope of use orfunctionality of described embodiments. For example, the computer system402 may include, but not limited to, a general-purpose computer, aprogrammed microprocessor, a micro-controller, a peripheral integratedcircuit element, and other devices or arrangements of devices that arecapable of implementing the steps that constitute the method of thepresent invention. In an embodiment of the present invention, the memory406 may store software for implementing various embodiments of thepresent invention. The computer system 402 may have additionalcomponents. For example, the computer system 402 includes one or morecommunication channels 408, one or more input devices 410, one or moreoutput devices 412, and storage 414. An interconnection mechanism (notshown) such as a bus, controller, or network, interconnects thecomponents of the computer system 402. In various embodiments of thepresent invention, operating system software (not shown) provides anoperating environment for various softwares executing in the computersystem 402, and manages different functionalities of the components ofthe computer system 402.

The communication channel(s) 408 allow communication over acommunication medium to various other computing entities. Thecommunication medium provides information such as program instructions,or other data in a communication media. The communication mediaincludes, but not limited to, wired or wireless methodologiesimplemented with an electrical, optical, RF, infrared, acoustic,microwave, bluetooth or other transmission media.

The input device(s) 410 may include, but not limited to, a keyboard,mouse, pen, joystick, trackball, a voice device, a scanning device, orany another device that is capable of providing input to the computersystem 402. In an embodiment of the present invention, the inputdevice(s) 410 may be a sound card or similar device that accepts audioinput in analog or digital form. The output device(s) 412 may include,but not limited to, a user interface on CRT or LCD, printer, speaker,CD/DVD writer, or any other device that provides output from thecomputer system 402.

The storage 414 may include, but not limited to, magnetic disks,magnetic tapes, CD-ROMs, CD-RWs, DVDs, flash drives or any other mediumwhich can be used to store information and can be accessed by thecomputer system 402. In various embodiments of the present invention,the storage 414 contains program instructions for implementing thedescribed embodiments.

The present invention may suitably be embodied as a computer programproduct for use with the computer system 402. The method describedherein is typically implemented as a computer program product,comprising a set of program instructions which is executed by thecomputer system 402 or any other similar device. The set of programinstructions may be a series of computer readable codes stored on atangible medium, such as a computer readable storage medium (storage414), for example, diskette, CD-ROM, ROM, flash drives or hard disk, ortransmittable to the computer system 402, via a modem or other interfacedevice, over either a tangible medium, including but not limited tooptical or analogue communications channel(s) 408. The implementation ofthe invention as a computer program product may be in an intangible formusing wireless techniques, including but not limited to microwave,infrared, bluetooth or other transmission techniques. These instructionscan be preloaded into a system or recorded on a storage medium such as aCD-ROM, or made available for downloading over a network such as theinternet or a mobile telephone network. The series of computer readableinstructions may embody all or part of the functionality previouslydescribed herein.

The present invention may be implemented in numerous ways including asan apparatus, method, or a computer program product such as a computerreadable storage medium or a computer network wherein programminginstructions are communicated from a remote location.

While the exemplary embodiments of the present invention are describedand illustrated herein, it will be appreciated that they are merelyillustrative. It will be understood by those skilled in the art thatvarious modifications in form and detail may be made therein withoutdeparting from or offending the spirit and scope of the invention asdefined by the appended claims.

We claim:
 1. A system for detecting retinopathy, the system comprising:an image input module configured to receive one or more fundus images; apre-processing module configured to apply one or more transformations tothe one or more received fundus images; a feature extraction moduleconfigured to extract one or more features from the one or moretransformed images using one or more Convolutional Neural Networks(CNNs); and a prediction module configured to determine stage ofretinopathy by classifying the one or more extracted features usingpre-stored features, wherein the pre-stored features are extracted fromone or more training fundus images by the one or more CNNs and furtherwherein each pre-stored feature corresponds to a class which isassociated with a predetermined stage of retinopathy.
 2. The system ofclaim 1, wherein the one or more transformations comprise at least oneof: contrast stretching and hue transformation.
 3. The system of claim1, wherein the one or more features are extracted from the one or moretransformed images by passing the one or more transformed images throughone or more layers of the CNNs thereby extracting details correspondingto one or more points of interest within the one or more transformedimages.
 4. The system of claim 1, wherein the one or more extractedfeatures are classified by matching each of the one or more extractedfeatures with the pre-stored features and further wherein the stage ofretinopathy is determined based on the predetermined stage ofretinopathy associated with the class corresponding to each of thematched pre-stored features.
 5. The system of claim 1 wherein the one ormore fundus images are of one or more patients.
 6. The system of claim1, wherein each training fundus image represents a specificpredetermined stage of retinopathy and is classified based on thespecific predetermined stage of retinopathy and further wherein thepre-stored features extracted from the training fundus image correspondsto the class associated with the training fundus image.
 7. The system ofclaim 1, wherein the predetermined stage of retinopathy is one of:absence of retinopathy, mild retinopathy, moderate retinopathy, severeretinopathy and proliferative retinopathy.
 8. A computer-implementedmethod for detecting retinopathy, via program instructions stored in amemory and executed by a processor, the computer-implemented methodcomprising: receiving one or more fundus images; applying one or moretransformations to the one or more received fundus images; extractingone or more features from the one or more transformed images using oneor more Convolutional Neural Networks (CNNs); and determining stage ofretinopathy by classifying the one or more extracted features usingpre-stored features, wherein the pre-stored features are extracted fromone or more training fundus images by the one or more CNNs and furtherwherein each pre-stored feature corresponds to a class which isassociated with a predetermined stage of retinopathy.
 9. Thecomputer-implemented method of claim 8, wherein the one or moretransformations comprise at least one of: contrast stretching and huetransformation.
 10. The computer-implemented method of claim 8, whereinthe one or more features are extracted from the one or more transformedimages by passing the one or more transformed images through one or morelayers of the CNNs thereby extracting details corresponding to one ormore points of interest within the one or more transformed images. 11.The computer-implemented method of claim 8, wherein the one or moreextracted features are classified by matching each of the one or moreextracted features with the pre-stored features and further wherein thestage of retinopathy is determined based on the predetermined stage ofretinopathy associated with the class corresponding to each of thematched pre-stored features.
 12. The computer-implemented method ofclaim 8, wherein the one or more fundus images are of one or morepatients.
 13. The computer-implemented method of claim 8, wherein eachtraining fundus image represents a specific predetermined stage ofretinopathy and is classified based on the specific predetermined stageof retinopathy and further wherein the pre-stored features extractedfrom the training fundus image corresponds to the class associated withthe training fundus image.
 14. The computer-implemented method of claim8, wherein the predetermined stage of retinopathy is one of: absence ofretinopathy, mild retinopathy, moderate retinopathy, severe retinopathyand proliferative retinopathy.
 15. A computer program product fordetecting retinopathy, the computer program product comprising: anon-transitory computer-readable medium having computer-readable programcode stored thereon, the computer-readable program code comprisinginstructions that when executed by a processor, cause the processor to:receive one or more fundus images; apply one or more transformations tothe one or more received fundus images; extract one or more featuresfrom the one or more transformed images using one or more ConvolutionalNeural Networks (CNNs); and determine stage of retinopathy byclassifying the one or more extracted features using pre-storedfeatures, wherein the pre-stored features are extracted from one or moretraining fundus images by the one or more CNNs and further wherein eachpre-stored feature corresponds to a class which is associated with apredetermined stage of retinopathy.